{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance evaluation and benchmarking\n",
    "\n",
    "In this notebook we will go step-by-step through the model evaluation part of our paper, as well as through the benchmarking part, where we compare our simulation results with a good hand-full of different (well-established) hydrological models.\n",
    "\n",
    "For more information read the experiment description in our paper:\n",
    "\n",
    "**TODO**: Include Ref\n",
    "\n",
    "Note:\n",
    "If you want to run this notebook locally and reproduce the figures of our paper\n",
    "- make sure you have our pre-trained models. See the [README.md](link) in the repository for further instructions.\n",
    "\n",
    "- make sure to have the CAMELS benchmark data set. See the [README.md](link) in the repository for further instructions.\n",
    "\n",
    "#### Adapt the lines below according to your local system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Needed if no precomputed results are used. Main directory containing all runs\n",
    "BASE_RUN_DIR = \"/datadisk/data/EALSTM_PAPER/runs/\"\n",
    "\n",
    "# Path to the benchmark model folders containing the basin netCDF files\n",
    "BENCHMARK_DIR = \"/datadisk/data/CAMELS/benchmark_models/combined/netcdf/\"\n",
    "\n",
    "# Path to the main directory of this repository\n",
    "BASE_CODE_DIR = \"/home/frederik/projects/ealstm_in_hydrology\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pickle\n",
    "import sys\n",
    "from collections import defaultdict\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import tqdm\n",
    "from scipy.stats import wilcoxon\n",
    "\n",
    "# Add repository to Python path\n",
    "sys.path.append(BASE_CODE_DIR)\n",
    "from papercode.plotutils import model_draw_style, model_specs, ecdf\n",
    "from papercode.evalutils import (get_run_dirs, eval_lstm_models, \n",
    "                                 eval_benchmark_models, get_pvals, \n",
    "                                 get_mean_basin_performance, get_cohens_d)\n",
    "from papercode.metrics import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definition of global variables. \n",
    "\n",
    "**Note**: If you want to recompute all model metrics, change the `PRECOMPUTED_DATA` flag to `False`. No GPU is required. If you want to use the pre-calculated metrics, make sure the flag is set to `True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If True load pre-computed metrics from pickle file, else re-calculate everything\n",
    "PRECOMPUTED_DATA = True\n",
    "\n",
    "# Convert to PosixPaths\n",
    "BASE_RUN_DIR = Path(BASE_RUN_DIR)\n",
    "BENCHMARK_DIR = Path(BENCHMARK_DIR)\n",
    "\n",
    "# Set of evaluation functions\n",
    "EVAL_FUNCS = {'NSE': calc_nse, \n",
    "              'alpha_nse': calc_alpha_nse, \n",
    "              'beta_nse': calc_beta_nse,\n",
    "              'FHV': calc_fdc_fhv, \n",
    "              'FLV': calc_fdc_flv, \n",
    "              'FMS': calc_fdc_fms}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate LSTM-based models\n",
    "\n",
    "First, we compare the 6 different settings of LSTM-based models, that we trained for this paper. These are three different model settings:\n",
    "\n",
    "1. EA-LSTM: Our proposed adaption of the LSTM recurrent neural network, where the static catchment characteristics are used to modulate the input gate.\n",
    "2. LSTM: The standard LSTM architecture, where the static catchment characteristics are concatenated to the meterological inputs at each time step.\n",
    "3. LSTM (no static inputs): A standard LSTM that is only trained using the meteorological forcing data.\n",
    "\n",
    "All three model configurations were trained using two different loss functions:\n",
    "\n",
    "1. MSELoss: The standard mean squared error loss.\n",
    "2. NSELoss: Our proposed loss function, which approximates the basin averaged NSE. For more details see the Method section of our manuscript.\n",
    "\n",
    "For each of the 6 settings, we trained 8 models (using different random initializations) and furthermore combined these 8 models to an ensemble (by averaging the k=8 model simulations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded data from pre-computed pickle file\n"
     ]
    }
   ],
   "source": [
    "if PRECOMPUTED_DATA:\n",
    "    print(\"Loaded data from pre-computed pickle file\")\n",
    "    with open(\"all_metrics.p\", \"rb\") as fp:\n",
    "        all_metrics = pickle.load(fp)\n",
    "else:\n",
    "    all_metrics = {}\n",
    "    for func_name, func in EVAL_FUNCS.items():\n",
    "        tqdm.tqdm.write(f\"Calculating metric: {func_name}\")\n",
    "        model_metrics = {}\n",
    "        for model, specs in model_specs.items():\n",
    "            run_dirs = get_run_dirs(root_dir=BASE_RUN_DIR, model=specs[\"model\"], loss=specs[\"loss\"])\n",
    "            model_metrics[model] = eval_lstm_models(run_dirs=run_dirs, func=func)\n",
    "\n",
    "        all_metrics[func_name] = model_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabular comparison\n",
    "\n",
    "In a first step, we look at the mean, median NSE as well as the number of catastrophic failures. Catastrophic failures are defined as the number of basins, where the model has a NSE <= 0.\n",
    "We calculate the mean of each of this three statistics over the model (n=8) model repetitions and report the standard deviation here as well. The repetitions are denoted as `ensemble=False` in the table below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>failures</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std_failures</th>\n",
       "      <th>std_mean</th>\n",
       "      <th>std_median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_type</th>\n",
       "      <th>ensemble</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">EA-LSTM MSE</th>\n",
       "      <th>False</th>\n",
       "      <td>9.125</td>\n",
       "      <td>0.631087</td>\n",
       "      <td>0.709609</td>\n",
       "      <td>0.927025</td>\n",
       "      <td>0.017903</td>\n",
       "      <td>0.005168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>6.000</td>\n",
       "      <td>0.679790</td>\n",
       "      <td>0.739772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">EA-LSTM NSE</th>\n",
       "      <th>False</th>\n",
       "      <td>2.750</td>\n",
       "      <td>0.673397</td>\n",
       "      <td>0.712292</td>\n",
       "      <td>0.968246</td>\n",
       "      <td>0.005964</td>\n",
       "      <td>0.004506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>2.000</td>\n",
       "      <td>0.704847</td>\n",
       "      <td>0.740911</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LSTM MSE</th>\n",
       "      <th>False</th>\n",
       "      <td>5.625</td>\n",
       "      <td>0.664064</td>\n",
       "      <td>0.726058</td>\n",
       "      <td>1.932453</td>\n",
       "      <td>0.012097</td>\n",
       "      <td>0.003281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>3.000</td>\n",
       "      <td>0.707204</td>\n",
       "      <td>0.757268</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LSTM NSE</th>\n",
       "      <th>False</th>\n",
       "      <td>1.750</td>\n",
       "      <td>0.686619</td>\n",
       "      <td>0.731623</td>\n",
       "      <td>0.661438</td>\n",
       "      <td>0.012865</td>\n",
       "      <td>0.002156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>2.000</td>\n",
       "      <td>0.719063</td>\n",
       "      <td>0.759361</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LSTM (no static inputs) MSE</th>\n",
       "      <th>False</th>\n",
       "      <td>44.250</td>\n",
       "      <td>0.235767</td>\n",
       "      <td>0.599360</td>\n",
       "      <td>3.960745</td>\n",
       "      <td>0.048782</td>\n",
       "      <td>0.004920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>31.000</td>\n",
       "      <td>0.357971</td>\n",
       "      <td>0.646646</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LSTM (no static inputs) NSE</th>\n",
       "      <th>False</th>\n",
       "      <td>28.000</td>\n",
       "      <td>0.393980</td>\n",
       "      <td>0.593262</td>\n",
       "      <td>3.427827</td>\n",
       "      <td>0.058873</td>\n",
       "      <td>0.008412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20.000</td>\n",
       "      <td>0.492559</td>\n",
       "      <td>0.642070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      failures      mean    median  \\\n",
       "model_type                  ensemble                                 \n",
       "EA-LSTM MSE                 False        9.125  0.631087  0.709609   \n",
       "                            True         6.000  0.679790  0.739772   \n",
       "EA-LSTM NSE                 False        2.750  0.673397  0.712292   \n",
       "                            True         2.000  0.704847  0.740911   \n",
       "LSTM MSE                    False        5.625  0.664064  0.726058   \n",
       "                            True         3.000  0.707204  0.757268   \n",
       "LSTM NSE                    False        1.750  0.686619  0.731623   \n",
       "                            True         2.000  0.719063  0.759361   \n",
       "LSTM (no static inputs) MSE False       44.250  0.235767  0.599360   \n",
       "                            True        31.000  0.357971  0.646646   \n",
       "LSTM (no static inputs) NSE False       28.000  0.393980  0.593262   \n",
       "                            True        20.000  0.492559  0.642070   \n",
       "\n",
       "                                      std_failures  std_mean  std_median  \n",
       "model_type                  ensemble                                      \n",
       "EA-LSTM MSE                 False         0.927025  0.017903    0.005168  \n",
       "                            True               NaN       NaN         NaN  \n",
       "EA-LSTM NSE                 False         0.968246  0.005964    0.004506  \n",
       "                            True               NaN       NaN         NaN  \n",
       "LSTM MSE                    False         1.932453  0.012097    0.003281  \n",
       "                            True               NaN       NaN         NaN  \n",
       "LSTM NSE                    False         0.661438  0.012865    0.002156  \n",
       "                            True               NaN       NaN         NaN  \n",
       "LSTM (no static inputs) MSE False         3.960745  0.048782    0.004920  \n",
       "                            True               NaN       NaN         NaN  \n",
       "LSTM (no static inputs) NSE False         3.427827  0.058873    0.008412  \n",
       "                            True               NaN       NaN         NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for model_type, models in all_metrics[\"NSE\"].items():\n",
    "    if model_type == \"benchmarks\":\n",
    "        continue\n",
    "    seeds = [k for k in models.keys() if \"seed\" in k]\n",
    "    means, medians, failures = [], [], []\n",
    "    for seed in seeds:\n",
    "        nses = list(models[seed].values())\n",
    "        means.append(np.mean(nses))\n",
    "        medians.append(np.median(nses))\n",
    "        failures.append(len([v for v in nses if v <= 0]))\n",
    "    data_sing = {'model_type': model_draw_style[model_type][\"label\"], \n",
    "                 'ensemble': False, \n",
    "                 'mean': np.mean(means), \n",
    "                 'std_mean': np.std(means),\n",
    "                 'median': np.mean(medians),\n",
    "                 'std_median': np.std(medians),\n",
    "                 'failures': np.mean(failures),\n",
    "                 'std_failures': np.std(failures)}\n",
    "    data.append(data_sing)\n",
    "    values = list(models[\"ensemble\"].values())\n",
    "    data_ensemble = {'model_type': model_draw_style[model_type][\"label\"],\n",
    "                   'ensemble': True,\n",
    "                   'mean': np.mean(values),\n",
    "                   'median': np.median(values),\n",
    "                   'failures': len([v for v in values if v < 0]) }\n",
    "    data.append(data_ensemble)\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df = df.set_index(keys=[\"model_type\", \"ensemble\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cumulative density function plot\n",
    "\n",
    "Here we look at the CDF of the NSEs for each of our 6 configurations. \n",
    "\n",
    "- `solid` lines mark models trained with `NSELoss`, `dashed` lines mark models trained with `MSELoss`\n",
    "- `saturated colors` are ensemble (n=8) means, `non-saturated colors` is a single model. Here we took seed 111 of each model, which is rather arbitrary but from the table above we seed that the mean/median NSE are robust between different random initializations.\n",
    "- `square` marker denote models trained with static features, while `triangle` markers denote models trained without static features\n",
    "- `green` is our proposed `EA-LSTM`, `orange` the standard LSTM with static features and `purple` the standard LSTM trained only on meteorological forcing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Effect of (not) using static catchment attributes')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "\n",
    "for model_type, models in all_metrics[\"NSE\"].items():\n",
    "    if 'lstm' in model_type:\n",
    "        # single seed\n",
    "        values = list(models['seed111'].values())\n",
    "        bin_, cdf_ = ecdf(values)\n",
    "        ax.plot(bin_,\n",
    "                cdf_,\n",
    "                label=f\"{model_draw_style[model_type]['label']} seed111\",\n",
    "                color=model_draw_style[model_type][\"single_color\"], \n",
    "                marker=model_draw_style[model_type]['marker'], \n",
    "                markevery=20, \n",
    "                linestyle=model_draw_style[model_type]['linestyle'])\n",
    "        \n",
    "        # ensemble seed\n",
    "        values = list(models['ensemble'].values())\n",
    "        bin_, cdf_ = ecdf(values)\n",
    "        ax.plot(bin_,\n",
    "                cdf_, \n",
    "                label=f\"{model_draw_style[model_type]['label']} ensemble (n=8)\", \n",
    "                color=model_draw_style[model_type]['ensemble_color'], \n",
    "                linestyle=model_draw_style[model_type]['linestyle'])\n",
    "    \n",
    "ax.set_xlim(0, 1)\n",
    "ax.grid(True)\n",
    "ax.legend(loc='upper left')\n",
    "ax.set_xlabel('NSE', fontsize=14)\n",
    "ax.set_ylabel('cumulative density', fontsize=14)\n",
    "ax.set_title(\"Effect of (not) using static catchment attributes\", fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate statistical significance.\n",
    "\n",
    "1. Calculate statistical significance between LSTM trained with and without static features using either MSE or NSE as loss function.\n",
    "\n",
    "2. Calculate statistical significance between EA-LSTM and standard LSTM (with static features)\n",
    "\n",
    "We always report the max, mean p-value between all possible seed combinations (n=8^2=64) as well as the p-value between the ensemble means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### NSE:\n",
      "With or without statics, optimized with MSE\n",
      "Single models: p-value=1.15e-75\n",
      "Ensemble mean: 3.99e-68\n",
      "\n",
      "With or without statics, optimized with NSE\n",
      "Single models: p-value=8.76e-81\n",
      "Ensemble mean: 3.32e-75\n",
      "\n",
      "EA-LSTM vs LSTM (with statics), optimized with MSE\n",
      "Single models: p-value=6.07e-20\n",
      "Ensemble mean: 2.90e-20\n",
      "\n",
      "EA-LSTM vs LSTM (with statics), optimized with NSE\n",
      "Single models: p-value=1.15e-28\n",
      "Ensemble mean: 2.11e-26\n",
      "Effect size using Cohen's d is: d=0.055\n",
      "\n",
      "EA-LSTM optimized with NSE vs. EA-LSTM optimized with MSE\n",
      "Single models: p-value=2.08e-01\n",
      "Ensemble mean: 4.38e-01\n",
      "\n",
      "LSTM  without static features optimized with NSE vs. optimized with MSE\n",
      "Single models: p-value=4.34e-02\n",
      "Ensemble mean: 8.09e-01\n"
     ]
    }
   ],
   "source": [
    "print(\"### NSE:\")\n",
    "print(\"With or without statics, optimized with MSE\")\n",
    "p_val_single, p_val_ensemble = get_pvals(all_metrics[\"NSE\"], \n",
    "                                         model1='lstm_no_static_MSE', \n",
    "                                         model2='lstm_MSE')\n",
    "print(f\"Single models: p-value={p_val_single:.2e}\")\n",
    "print(f\"Ensemble mean: {p_val_ensemble:.2e}\")\n",
    "\n",
    "print(\"\\nWith or without statics, optimized with NSE\")\n",
    "p_val_single, p_val_ensemble = get_pvals(all_metrics[\"NSE\"], \n",
    "                                         model1='lstm_no_static_NSE', \n",
    "                                         model2='lstm_NSE')\n",
    "print(f\"Single models: p-value={p_val_single:.2e}\")\n",
    "print(f\"Ensemble mean: {p_val_ensemble:.2e}\")\n",
    "\n",
    "print(\"\\nEA-LSTM vs LSTM (with statics), optimized with MSE\")\n",
    "p_val_single, p_val_ensemble = get_pvals(all_metrics[\"NSE\"], \n",
    "                                         model1='lstm_MSE', \n",
    "                                         model2='ealstm_MSE')\n",
    "print(f\"Single models: p-value={p_val_single:.2e}\")\n",
    "print(f\"Ensemble mean: {p_val_ensemble:.2e}\")\n",
    "\n",
    "print(\"\\nEA-LSTM vs LSTM (with statics), optimized with NSE\")\n",
    "p_val_single, p_val_ensemble = get_pvals(all_metrics[\"NSE\"], \n",
    "                                         model1='lstm_NSE', \n",
    "                                         model2='ealstm_NSE')\n",
    "print(f\"Single models: p-value={p_val_single:.2e}\")\n",
    "print(f\"Ensemble mean: {p_val_ensemble:.2e}\")\n",
    "\n",
    "values1 = get_mean_basin_performance(all_metrics[\"NSE\"], model=\"ealstm_NSE\")\n",
    "values1 = list(values1.values())\n",
    "values2 = get_mean_basin_performance(all_metrics[\"NSE\"], model=\"lstm_NSE\")\n",
    "values2 = list(values2.values())\n",
    "d = get_cohens_d(values1, values2)\n",
    "print(f\"Effect size using Cohen's d is: d={d:.3f}\")\n",
    "\n",
    "\n",
    "print(\"\\nEA-LSTM optimized with NSE vs. EA-LSTM optimized with MSE\")\n",
    "p_val_single, p_val_ensemble = get_pvals(all_metrics[\"NSE\"], \n",
    "                                         model1='ealstm_NSE', \n",
    "                                         model2='ealstm_MSE')\n",
    "print(f\"Single models: p-value={p_val_single:.2e}\")\n",
    "print(f\"Ensemble mean: {p_val_ensemble:.2e}\")\n",
    "\n",
    "print(\"\\nLSTM  without static features optimized with NSE vs. optimized with MSE\")\n",
    "p_val_single, p_val_ensemble = get_pvals(all_metrics[\"NSE\"], \n",
    "                                         model1='lstm_no_static_MSE', \n",
    "                                         model2='lstm_no_static_NSE')\n",
    "print(f\"Single models: p-value={p_val_single:.2e}\")\n",
    "print(f\"Ensemble mean: {p_val_ensemble:.2e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare against benchmark models\n",
    "\n",
    "Now we compare our model, the `EA-LSTM` optimized with `NSELoss`, against the set of benchmark models. Here, we only use the model results from the set of basins that were modeled by all models (the benchmark models and our models).\n",
    "\n",
    "First, we have to calculate the metrics for all basins and benchmark models (or load the data from the precomputed file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation data of benchmark models already exist in pre-computed data\n"
     ]
    }
   ],
   "source": [
    "if not PRECOMPUTED_DATA:\n",
    "    for metric in all_metrics.keys():\n",
    "        tqdm.tqdm.write(f\"Calculating metric: {metric}\")\n",
    "        all_metrics[metric][\"benchmarks\"] = eval_benchmark_models(netcdf_folder=BENCHMARK_DIR, \n",
    "                                                                  func=EVAL_FUNCS[metric])\n",
    "else:\n",
    "    print(\"Evaluation data of benchmark models already exist in pre-computed data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not PRECOMPUTED_DATA:\n",
    "    with open(\"all_metrics.p\", \"wb\") as fp:\n",
    "        pickle.dump(all_metrics, fp)\n",
    "    print(\"Stored precomputed data in 'all_metrics.p'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "447"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find all basins modeled by all benchmarks\n",
    "basins = frozenset(list(all_metrics[\"NSE\"][\"ealstm_NSE\"][\"ensemble\"].keys()))\n",
    "for model, results in all_metrics[\"NSE\"][\"benchmarks\"].items():\n",
    "    basins = basins.intersection(list(results.keys()))\n",
    "len(basins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get subset of all metrics for these share basins\n",
    "sub_metrics = {metric: defaultdict(dict) for metric in all_metrics.keys()}\n",
    "for metric, model_metric in all_metrics.items():\n",
    "    for model_type, models in model_metric.items():\n",
    "        for model, results in models.items():\n",
    "            sub_metrics[metric][model_type][model] = {}\n",
    "            for basin, nse in results.items():\n",
    "                if basin in basins:\n",
    "                    sub_metrics[metric][model_type][model][basin] = nse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Benchmarking against CONUS-wide calibrated hydrological models')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "\n",
    "for model_type, models in sub_metrics[\"NSE\"].items():\n",
    "    if (model_type == \"ealstm_NSE\") or (model_type == \"lstm_no_static_NSE\"):\n",
    "        # single seed\n",
    "        values = list(models['seed111'].values())\n",
    "        bin_, cdf_ = ecdf(values)\n",
    "        ax.plot(bin_,\n",
    "                cdf_,\n",
    "                label=f\"{model_draw_style[model_type]['label']} seed111\",\n",
    "                color=model_draw_style[model_type][\"single_color\"], \n",
    "                marker=model_draw_style[model_type]['marker'], \n",
    "                markevery=20, \n",
    "                linestyle=model_draw_style[model_type]['linestyle'])\n",
    "        \n",
    "        # ensemble seed\n",
    "        values = list(models['ensemble'].values())\n",
    "        bin_, cdf_ = ecdf(values)\n",
    "        ax.plot(bin_,\n",
    "                cdf_, \n",
    "                label=f\"{model_draw_style[model_type]['label']} ensemble (n=8)\", \n",
    "                color=model_draw_style[model_type]['ensemble_color'], \n",
    "                linestyle=model_draw_style[model_type]['linestyle'])\n",
    "    elif model_type == \"benchmarks\":\n",
    "        for benchmark_model, benchmark_result in models.items():\n",
    "            if \"conus\" in benchmark_model:\n",
    "                values = list(benchmark_result.values())\n",
    "                bin_, cdf_ = ecdf(values)\n",
    "                ax.plot(bin_,\n",
    "                        cdf_, \n",
    "                        label=model_draw_style[benchmark_model]['label'], \n",
    "                        color=model_draw_style[benchmark_model]['color'], \n",
    "                        linestyle=model_draw_style[benchmark_model]['linestyle'])\n",
    "    \n",
    "ax.set_xlim(0, 1)\n",
    "ax.grid(True)\n",
    "ax.legend(loc='upper left')\n",
    "ax.set_xlabel('NSE', fontsize=14)\n",
    "ax.set_ylabel('cumulative density', fontsize=14)\n",
    "ax.set_title(\"Benchmarking against CONUS-wide calibrated hydrological models\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VIC is better (or equal) than EA-LSTM ensemble mean in 2/447 basins\n",
      "mHm is better (or equal) than EA-LSTM ensemble mean in 16/447 basins\n"
     ]
    }
   ],
   "source": [
    "vic_count = 0\n",
    "mhm_count = 0\n",
    "for basin in basins:\n",
    "    lstm_nse = sub_metrics[\"NSE\"][\"ealstm_NSE\"][\"ensemble\"][basin]\n",
    "    if sub_metrics[\"NSE\"][\"benchmarks\"][\"VIC_conus\"][basin] >= lstm_nse:\n",
    "        vic_count += 1\n",
    "    if sub_metrics[\"NSE\"][\"benchmarks\"][\"mHm_conus\"][basin] >= lstm_nse:\n",
    "        mhm_count += 1\n",
    "        \n",
    "print(f\"VIC is better (or equal) than EA-LSTM ensemble mean in {vic_count}/{len(basins)} basins\")\n",
    "print(f\"mHm is better (or equal) than EA-LSTM ensemble mean in {mhm_count}/{len(basins)} basins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Benchmarking against basin-wise calibrated hydrological models')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "\n",
    "for model_type, models in sub_metrics[\"NSE\"].items():\n",
    "    if model_type == \"ealstm_NSE\":\n",
    "        # single seed\n",
    "        values = list(models['seed111'].values())\n",
    "        bin_, cdf_ = ecdf(values)\n",
    "        ax.plot(bin_,\n",
    "                cdf_,\n",
    "                label=f\"{model_draw_style[model_type]['label']} seed111\",\n",
    "                color=model_draw_style[model_type][\"single_color\"], \n",
    "                marker=model_draw_style[model_type]['marker'], \n",
    "                markevery=20, \n",
    "                linestyle=model_draw_style[model_type]['linestyle'])\n",
    "        \n",
    "        # ensemble seed\n",
    "        values = list(models['ensemble'].values())\n",
    "        bin_, cdf_ = ecdf(values)\n",
    "        ax.plot(bin_,\n",
    "                cdf_, \n",
    "                label=f\"{model_draw_style[model_type]['label']} ensemble (n=8)\", \n",
    "                color=model_draw_style[model_type]['ensemble_color'], \n",
    "                linestyle=model_draw_style[model_type]['linestyle'])\n",
    "    elif model_type == \"benchmarks\":\n",
    "        for benchmark_model, benchmark_result in models.items():\n",
    "            if not \"conus\" in benchmark_model:\n",
    "                values = list(benchmark_result.values())\n",
    "                bin_, cdf_ = ecdf(values)\n",
    "                ax.plot(bin_,\n",
    "                        cdf_, \n",
    "                        label=model_draw_style[benchmark_model]['label'], \n",
    "                        color=model_draw_style[benchmark_model]['color'], \n",
    "                        linestyle=model_draw_style[benchmark_model]['linestyle'])\n",
    "    \n",
    "ax.set_xlim(0, 1)\n",
    "ax.grid(True)\n",
    "ax.legend(loc='upper left')\n",
    "ax.set_xlabel('NSE', fontsize=14)\n",
    "ax.set_ylabel('cumulative density', fontsize=14)\n",
    "ax.set_title(\"Benchmarking against basin-wise calibrated hydrological models\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>FHV median</th>\n",
       "      <th>FHV median std</th>\n",
       "      <th>FLV median</th>\n",
       "      <th>FLV median std</th>\n",
       "      <th>FMS median</th>\n",
       "      <th>FMS median std</th>\n",
       "      <th>NSE mean</th>\n",
       "      <th>NSE mean std</th>\n",
       "      <th>NSE median</th>\n",
       "      <th>NSE median std</th>\n",
       "      <th>alpha_nse median</th>\n",
       "      <th>alpha_nse median std</th>\n",
       "      <th>beta_nse median</th>\n",
       "      <th>beta_nse median std</th>\n",
       "      <th>failures</th>\n",
       "      <th>failures std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>ensemble</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">EA-LSTM with NSE</th>\n",
       "      <th>False</th>\n",
       "      <td>-16.912275</td>\n",
       "      <td>1.148501</td>\n",
       "      <td>2.007990</td>\n",
       "      <td>7.588762</td>\n",
       "      <td>-10.012182</td>\n",
       "      <td>1.724323</td>\n",
       "      <td>0.673587</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>0.713536</td>\n",
       "      <td>0.004498</td>\n",
       "      <td>0.824198</td>\n",
       "      <td>0.013147</td>\n",
       "      <td>-0.029563</td>\n",
       "      <td>0.008962</td>\n",
       "      <td>2.125</td>\n",
       "      <td>1.053269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>-18.140744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.937520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.290716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.705011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.742259</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.808083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.027088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAC-SMA</th>\n",
       "      <th>False</th>\n",
       "      <td>-20.356397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.408956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.282640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.563865</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.602803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.778563</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.065661</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIC (basin-wise calibrated)</th>\n",
       "      <th>False</th>\n",
       "      <td>-28.139453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-69.958477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.561743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.518387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.551317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.724608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.017847</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIC (CONUS-wide calibrated)</th>\n",
       "      <th>False</th>\n",
       "      <td>-56.483333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.362716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-27.989592</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.167402</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.306962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.456623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.074212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mHm (basin-wise calibrated)</th>\n",
       "      <th>False</th>\n",
       "      <td>-18.640055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.433140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.221696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.627171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.665944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.806612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.040295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mHm (CONUS-wide calibrated)</th>\n",
       "      <th>False</th>\n",
       "      <td>-40.178353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.399823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-30.352985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.441506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.527395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.588520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.039348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HBV lower bound (n=1000 uncalibrated)</th>\n",
       "      <th>False</th>\n",
       "      <td>-41.859371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.882161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-15.942301</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.237386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.416526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.584043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.022660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HBV upper bound (n=100 calibrated)</th>\n",
       "      <th>False</th>\n",
       "      <td>-18.490541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.340945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-24.935201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.631317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.675633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.788359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUSE (seed 900)</th>\n",
       "      <th>False</th>\n",
       "      <td>-18.934932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.399515</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.091586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.586676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.638926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.795536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.030607</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUSE (seed 902)</th>\n",
       "      <th>False</th>\n",
       "      <td>-19.360154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-33.153355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.597864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.611049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.650483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.802053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.047413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUSE (seed 904)</th>\n",
       "      <th>False</th>\n",
       "      <td>-21.406883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-66.744506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.490393</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.582477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.622248</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.783042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.067400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                FHV median  FHV median std  \\\n",
       "model                                 ensemble                               \n",
       "EA-LSTM with NSE                      False     -16.912275        1.148501   \n",
       "                                      True      -18.140744             NaN   \n",
       "SAC-SMA                               False     -20.356397             NaN   \n",
       "VIC (basin-wise calibrated)           False     -28.139453             NaN   \n",
       "VIC (CONUS-wide calibrated)           False     -56.483333             NaN   \n",
       "mHm (basin-wise calibrated)           False     -18.640055             NaN   \n",
       "mHm (CONUS-wide calibrated)           False     -40.178353             NaN   \n",
       "HBV lower bound (n=1000 uncalibrated) False     -41.859371             NaN   \n",
       "HBV upper bound (n=100 calibrated)    False     -18.490541             NaN   \n",
       "FUSE (seed 900)                       False     -18.934932             NaN   \n",
       "FUSE (seed 902)                       False     -19.360154             NaN   \n",
       "FUSE (seed 904)                       False     -21.406883             NaN   \n",
       "\n",
       "                                                FLV median  FLV median std  \\\n",
       "model                                 ensemble                               \n",
       "EA-LSTM with NSE                      False       2.007990        7.588762   \n",
       "                                      True       31.937520             NaN   \n",
       "SAC-SMA                               False      37.408956             NaN   \n",
       "VIC (basin-wise calibrated)           False     -69.958477             NaN   \n",
       "VIC (CONUS-wide calibrated)           False      17.362716             NaN   \n",
       "mHm (basin-wise calibrated)           False      11.433140             NaN   \n",
       "mHm (CONUS-wide calibrated)           False      36.399823             NaN   \n",
       "HBV lower bound (n=1000 uncalibrated) False      23.882161             NaN   \n",
       "HBV upper bound (n=100 calibrated)    False      18.340945             NaN   \n",
       "FUSE (seed 900)                       False     -11.399515             NaN   \n",
       "FUSE (seed 902)                       False     -33.153355             NaN   \n",
       "FUSE (seed 904)                       False     -66.744506             NaN   \n",
       "\n",
       "                                                FMS median  FMS median std  \\\n",
       "model                                 ensemble                               \n",
       "EA-LSTM with NSE                      False     -10.012182        1.724323   \n",
       "                                      True      -11.290716             NaN   \n",
       "SAC-SMA                               False     -14.282640             NaN   \n",
       "VIC (basin-wise calibrated)           False      -6.561743             NaN   \n",
       "VIC (CONUS-wide calibrated)           False     -27.989592             NaN   \n",
       "mHm (basin-wise calibrated)           False      -7.221696             NaN   \n",
       "mHm (CONUS-wide calibrated)           False     -30.352985             NaN   \n",
       "HBV lower bound (n=1000 uncalibrated) False     -15.942301             NaN   \n",
       "HBV upper bound (n=100 calibrated)    False     -24.935201             NaN   \n",
       "FUSE (seed 900)                       False      -5.091586             NaN   \n",
       "FUSE (seed 902)                       False       9.597864             NaN   \n",
       "FUSE (seed 904)                       False      15.490393             NaN   \n",
       "\n",
       "                                                NSE mean  NSE mean std  \\\n",
       "model                                 ensemble                           \n",
       "EA-LSTM with NSE                      False     0.673587      0.006329   \n",
       "                                      True      0.705011           NaN   \n",
       "SAC-SMA                               False     0.563865           NaN   \n",
       "VIC (basin-wise calibrated)           False     0.518387           NaN   \n",
       "VIC (CONUS-wide calibrated)           False     0.167402           NaN   \n",
       "mHm (basin-wise calibrated)           False     0.627171           NaN   \n",
       "mHm (CONUS-wide calibrated)           False     0.441506           NaN   \n",
       "HBV lower bound (n=1000 uncalibrated) False     0.237386           NaN   \n",
       "HBV upper bound (n=100 calibrated)    False     0.631317           NaN   \n",
       "FUSE (seed 900)                       False     0.586676           NaN   \n",
       "FUSE (seed 902)                       False     0.611049           NaN   \n",
       "FUSE (seed 904)                       False     0.582477           NaN   \n",
       "\n",
       "                                                NSE median  NSE median std  \\\n",
       "model                                 ensemble                               \n",
       "EA-LSTM with NSE                      False       0.713536        0.004498   \n",
       "                                      True        0.742259             NaN   \n",
       "SAC-SMA                               False       0.602803             NaN   \n",
       "VIC (basin-wise calibrated)           False       0.551317             NaN   \n",
       "VIC (CONUS-wide calibrated)           False       0.306962             NaN   \n",
       "mHm (basin-wise calibrated)           False       0.665944             NaN   \n",
       "mHm (CONUS-wide calibrated)           False       0.527395             NaN   \n",
       "HBV lower bound (n=1000 uncalibrated) False       0.416526             NaN   \n",
       "HBV upper bound (n=100 calibrated)    False       0.675633             NaN   \n",
       "FUSE (seed 900)                       False       0.638926             NaN   \n",
       "FUSE (seed 902)                       False       0.650483             NaN   \n",
       "FUSE (seed 904)                       False       0.622248             NaN   \n",
       "\n",
       "                                                alpha_nse median  \\\n",
       "model                                 ensemble                     \n",
       "EA-LSTM with NSE                      False             0.824198   \n",
       "                                      True              0.808083   \n",
       "SAC-SMA                               False             0.778563   \n",
       "VIC (basin-wise calibrated)           False             0.724608   \n",
       "VIC (CONUS-wide calibrated)           False             0.456623   \n",
       "mHm (basin-wise calibrated)           False             0.806612   \n",
       "mHm (CONUS-wide calibrated)           False             0.588520   \n",
       "HBV lower bound (n=1000 uncalibrated) False             0.584043   \n",
       "HBV upper bound (n=100 calibrated)    False             0.788359   \n",
       "FUSE (seed 900)                       False             0.795536   \n",
       "FUSE (seed 902)                       False             0.802053   \n",
       "FUSE (seed 904)                       False             0.783042   \n",
       "\n",
       "                                                alpha_nse median std  \\\n",
       "model                                 ensemble                         \n",
       "EA-LSTM with NSE                      False                 0.013147   \n",
       "                                      True                       NaN   \n",
       "SAC-SMA                               False                      NaN   \n",
       "VIC (basin-wise calibrated)           False                      NaN   \n",
       "VIC (CONUS-wide calibrated)           False                      NaN   \n",
       "mHm (basin-wise calibrated)           False                      NaN   \n",
       "mHm (CONUS-wide calibrated)           False                      NaN   \n",
       "HBV lower bound (n=1000 uncalibrated) False                      NaN   \n",
       "HBV upper bound (n=100 calibrated)    False                      NaN   \n",
       "FUSE (seed 900)                       False                      NaN   \n",
       "FUSE (seed 902)                       False                      NaN   \n",
       "FUSE (seed 904)                       False                      NaN   \n",
       "\n",
       "                                                beta_nse median  \\\n",
       "model                                 ensemble                    \n",
       "EA-LSTM with NSE                      False           -0.029563   \n",
       "                                      True            -0.027088   \n",
       "SAC-SMA                               False           -0.065661   \n",
       "VIC (basin-wise calibrated)           False           -0.017847   \n",
       "VIC (CONUS-wide calibrated)           False           -0.074212   \n",
       "mHm (basin-wise calibrated)           False           -0.040295   \n",
       "mHm (CONUS-wide calibrated)           False           -0.039348   \n",
       "HBV lower bound (n=1000 uncalibrated) False           -0.022660   \n",
       "HBV upper bound (n=100 calibrated)    False           -0.011978   \n",
       "FUSE (seed 900)                       False           -0.030607   \n",
       "FUSE (seed 902)                       False           -0.047413   \n",
       "FUSE (seed 904)                       False           -0.067400   \n",
       "\n",
       "                                                beta_nse median std  failures  \\\n",
       "model                                 ensemble                                  \n",
       "EA-LSTM with NSE                      False                0.008962     2.125   \n",
       "                                      True                      NaN     1.000   \n",
       "SAC-SMA                               False                     NaN    13.000   \n",
       "VIC (basin-wise calibrated)           False                     NaN    10.000   \n",
       "VIC (CONUS-wide calibrated)           False                     NaN    41.000   \n",
       "mHm (basin-wise calibrated)           False                     NaN     7.000   \n",
       "mHm (CONUS-wide calibrated)           False                     NaN    29.000   \n",
       "HBV lower bound (n=1000 uncalibrated) False                     NaN    35.000   \n",
       "HBV upper bound (n=100 calibrated)    False                     NaN     9.000   \n",
       "FUSE (seed 900)                       False                     NaN    12.000   \n",
       "FUSE (seed 902)                       False                     NaN    10.000   \n",
       "FUSE (seed 904)                       False                     NaN     9.000   \n",
       "\n",
       "                                                failures std  \n",
       "model                                 ensemble                \n",
       "EA-LSTM with NSE                      False         1.053269  \n",
       "                                      True               NaN  \n",
       "SAC-SMA                               False              NaN  \n",
       "VIC (basin-wise calibrated)           False              NaN  \n",
       "VIC (CONUS-wide calibrated)           False              NaN  \n",
       "mHm (basin-wise calibrated)           False              NaN  \n",
       "mHm (CONUS-wide calibrated)           False              NaN  \n",
       "HBV lower bound (n=1000 uncalibrated) False              NaN  \n",
       "HBV upper bound (n=100 calibrated)    False              NaN  \n",
       "FUSE (seed 900)                       False              NaN  \n",
       "FUSE (seed 902)                       False              NaN  \n",
       "FUSE (seed 904)                       False              NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "single_model = {'model': 'EA-LSTM with NSE', 'ensemble': False}\n",
    "ensemble_mean = {'model': 'EA-LSTM with NSE', 'ensemble': True}\n",
    "# get EA-LSTM stats for all metrics\n",
    "for metric, metric_data in sub_metrics.items():\n",
    "    \n",
    "    # average over single models\n",
    "    seeds = [k for k in metric_data[\"ealstm_NSE\"].keys() if \"seed\" in k]\n",
    "    seed_vals = defaultdict(list)\n",
    "    for seed in seeds:\n",
    "        values = list(metric_data[\"ealstm_NSE\"][seed].values())\n",
    "        seed_vals[f\"{metric} median\"].append(np.median(values))\n",
    "        if metric == \"NSE\":\n",
    "            seed_vals[f\"{metric} mean\"].append(np.mean(values))\n",
    "            seed_vals[\"failures\"].append(len([v for v in values if v <= 0]))\n",
    "        single_model[f\"{metric} median\"] = np.mean(seed_vals[f\"{metric} median\"])\n",
    "        single_model[f\"{metric} median std\"] = np.std(seed_vals[f\"{metric} median\"])\n",
    "        if metric == \"NSE\":\n",
    "            single_model[f\"{metric} mean\"] = np.mean(seed_vals[f\"{metric} mean\"])\n",
    "            single_model[f\"{metric} mean std\"] = np.std(seed_vals[f\"{metric} mean\"])\n",
    "            single_model[f\"failures\"] = np.mean(seed_vals[\"failures\"])\n",
    "            single_model[f\"failures std\"] = np.std(seed_vals[\"failures\"])\n",
    "            \n",
    "    # ensemble mean\n",
    "    values = list(metric_data[\"ealstm_NSE\"][\"ensemble\"].values())\n",
    "    ensemble_mean[f\"{metric} median\"] = np.median(values)\n",
    "    if metric == \"NSE\":\n",
    "        ensemble_mean[\"NSE mean\"] = np.mean(values)\n",
    "        ensemble_mean[\"failures\"] = len([v for v in values if v <= 0])\n",
    "        \n",
    "data.append(single_model)\n",
    "data.append(ensemble_mean)\n",
    "        \n",
    "# benchmark models:\n",
    "for model in model_draw_style.keys():\n",
    "    if \"lstm\" in model:\n",
    "        continue\n",
    "    model_data = {\"model\": model_draw_style[model][\"label\"], \"ensemble\": False}\n",
    "    for metric, metric_data in sub_metrics.items():\n",
    "        values = list(metric_data[\"benchmarks\"][model].values())\n",
    "        model_data[f\"{metric} median\"] = np.median(values)\n",
    "        if metric == \"NSE\":\n",
    "            model_data[\"NSE mean\"] = np.mean(values)\n",
    "            model_data[\"failures\"] = len([v for v in values if v <= 0])\n",
    "            \n",
    "    data.append(model_data)\n",
    "        \n",
    "df = pd.DataFrame(data)\n",
    "df = df.set_index(keys=[\"model\", \"ensemble\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<table border=\"1\" class=\"dataframe\">\\n  <thead>\\n    <tr style=\"text-align: right;\">\\n      <th></th>\\n      <th></th>\\n      <th>FHV median</th>\\n      <th>FHV median std</th>\\n      <th>FLV median</th>\\n      <th>FLV median std</th>\\n      <th>FMS median</th>\\n      <th>FMS median std</th>\\n      <th>NSE mean</th>\\n      <th>NSE mean std</th>\\n      <th>NSE median</th>\\n      <th>NSE median std</th>\\n      <th>alpha_nse median</th>\\n      <th>alpha_nse median std</th>\\n      <th>beta_nse median</th>\\n      <th>beta_nse median std</th>\\n      <th>failures</th>\\n      <th>failures std</th>\\n    </tr>\\n    <tr>\\n      <th>model</th>\\n      <th>ensemble</th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n      <th></th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th rowspan=\"2\" valign=\"top\">EA-LSTM with NSE</th>\\n      <th>False</th>\\n      <td>-16.912275</td>\\n      <td>1.148501</td>\\n      <td>2.007990</td>\\n      <td>7.588762</td>\\n      <td>-10.012182</td>\\n      <td>1.724323</td>\\n      <td>0.673587</td>\\n      <td>0.006329</td>\\n      <td>0.713536</td>\\n      <td>0.004498</td>\\n      <td>0.824198</td>\\n      <td>0.013147</td>\\n      <td>-0.029563</td>\\n      <td>0.008962</td>\\n      <td>2.125</td>\\n      <td>1.053269</td>\\n    </tr>\\n    <tr>\\n      <th>True</th>\\n      <td>-18.140744</td>\\n      <td>NaN</td>\\n      <td>31.937520</td>\\n      <td>NaN</td>\\n      <td>-11.290716</td>\\n      <td>NaN</td>\\n      <td>0.705011</td>\\n      <td>NaN</td>\\n      <td>0.742259</td>\\n      <td>NaN</td>\\n      <td>0.808083</td>\\n      <td>NaN</td>\\n      <td>-0.027088</td>\\n      <td>NaN</td>\\n      <td>1.000</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>SAC-SMA</th>\\n      <th>False</th>\\n      <td>-20.356397</td>\\n      <td>NaN</td>\\n      <td>37.408956</td>\\n      <td>NaN</td>\\n      <td>-14.282640</td>\\n      <td>NaN</td>\\n      <td>0.563865</td>\\n      <td>NaN</td>\\n      <td>0.602803</td>\\n      <td>NaN</td>\\n      <td>0.778563</td>\\n      <td>NaN</td>\\n      <td>-0.065661</td>\\n      <td>NaN</td>\\n      <td>13.000</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>VIC (basin-wise calibrated)</th>\\n      <th>False</th>\\n      <td>-28.139453</td>\\n      <td>NaN</td>\\n      <td>-69.958477</td>\\n      <td>NaN</td>\\n      <td>-6.561743</td>\\n      <td>NaN</td>\\n      <td>0.518387</td>\\n      <td>NaN</td>\\n      <td>0.551317</td>\\n      <td>NaN</td>\\n      <td>0.724608</td>\\n      <td>NaN</td>\\n      <td>-0.017847</td>\\n      <td>NaN</td>\\n      <td>10.000</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>VIC (CONUS-wide calibrated)</th>\\n      <th>False</th>\\n      <td>-56.483333</td>\\n      <td>NaN</td>\\n      <td>17.362716</td>\\n      <td>NaN</td>\\n      <td>-27.989592</td>\\n      <td>NaN</td>\\n      <td>0.167402</td>\\n      <td>NaN</td>\\n      <td>0.306962</td>\\n      <td>NaN</td>\\n      <td>0.456623</td>\\n      <td>NaN</td>\\n      <td>-0.074212</td>\\n      <td>NaN</td>\\n      <td>41.000</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>mHm (basin-wise calibrated)</th>\\n      <th>False</th>\\n      <td>-18.640055</td>\\n      <td>NaN</td>\\n      <td>11.433140</td>\\n      <td>NaN</td>\\n      <td>-7.221696</td>\\n      <td>NaN</td>\\n      <td>0.627171</td>\\n      <td>NaN</td>\\n      <td>0.665944</td>\\n      <td>NaN</td>\\n      <td>0.806612</td>\\n      <td>NaN</td>\\n      <td>-0.040295</td>\\n      <td>NaN</td>\\n      <td>7.000</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>mHm (CONUS-wide calibrated)</th>\\n      <th>False</th>\\n      <td>-40.178353</td>\\n      <td>NaN</td>\\n      <td>36.399823</td>\\n      <td>NaN</td>\\n      <td>-30.352985</td>\\n      <td>NaN</td>\\n      <td>0.441506</td>\\n      <td>NaN</td>\\n      <td>0.527395</td>\\n      <td>NaN</td>\\n      <td>0.588520</td>\\n      <td>NaN</td>\\n      <td>-0.039348</td>\\n      <td>NaN</td>\\n      <td>29.000</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>HBV lower bound (n=1000 uncalibrated)</th>\\n      <th>False</th>\\n      <td>-41.859371</td>\\n      <td>NaN</td>\\n      <td>23.882161</td>\\n      <td>NaN</td>\\n      <td>-15.942301</td>\\n      <td>NaN</td>\\n      <td>0.237386</td>\\n      <td>NaN</td>\\n      <td>0.416526</td>\\n      <td>NaN</td>\\n      <td>0.584043</td>\\n      <td>NaN</td>\\n      <td>-0.022660</td>\\n      <td>NaN</td>\\n      <td>35.000</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>HBV upper bound (n=100 calibrated)</th>\\n      <th>False</th>\\n      <td>-18.490541</td>\\n      <td>NaN</td>\\n      <td>18.340945</td>\\n      <td>NaN</td>\\n      <td>-24.935201</td>\\n      <td>NaN</td>\\n      <td>0.631317</td>\\n      <td>NaN</td>\\n      <td>0.675633</td>\\n      <td>NaN</td>\\n      <td>0.788359</td>\\n      <td>NaN</td>\\n      <td>-0.011978</td>\\n      <td>NaN</td>\\n      <td>9.000</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>FUSE (seed 900)</th>\\n      <th>False</th>\\n      <td>-18.934932</td>\\n      <td>NaN</td>\\n      <td>-11.399515</td>\\n      <td>NaN</td>\\n      <td>-5.091586</td>\\n      <td>NaN</td>\\n      <td>0.586676</td>\\n      <td>NaN</td>\\n      <td>0.638926</td>\\n      <td>NaN</td>\\n      <td>0.795536</td>\\n      <td>NaN</td>\\n      <td>-0.030607</td>\\n      <td>NaN</td>\\n      <td>12.000</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>FUSE (seed 902)</th>\\n      <th>False</th>\\n      <td>-19.360154</td>\\n      <td>NaN</td>\\n      <td>-33.153355</td>\\n      <td>NaN</td>\\n      <td>9.597864</td>\\n      <td>NaN</td>\\n      <td>0.611049</td>\\n      <td>NaN</td>\\n      <td>0.650483</td>\\n      <td>NaN</td>\\n      <td>0.802053</td>\\n      <td>NaN</td>\\n      <td>-0.047413</td>\\n      <td>NaN</td>\\n      <td>10.000</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>FUSE (seed 904)</th>\\n      <th>False</th>\\n      <td>-21.406883</td>\\n      <td>NaN</td>\\n      <td>-66.744506</td>\\n      <td>NaN</td>\\n      <td>15.490393</td>\\n      <td>NaN</td>\\n      <td>0.582477</td>\\n      <td>NaN</td>\\n      <td>0.622248</td>\\n      <td>NaN</td>\\n      <td>0.783042</td>\\n      <td>NaN</td>\\n      <td>-0.067400</td>\\n      <td>NaN</td>\\n      <td>9.000</td>\\n      <td>NaN</td>\\n    </tr>\\n  </tbody>\\n</table>'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "#### NSE\n",
      "Comparison between EA-LSTM (optimized with NSE) and mHm (basin-wise calibrated)\n",
      "For single models: 4.29e-06\n",
      "For ensemble mean 1.01e-13\n",
      "Effect size: Single model d=0.256, ensemble mean d=0.445\n",
      "\n",
      "Comparison between EA-LSTM (optimized with NSE) and HBV (upper limit)\n",
      "For single models: 1.90e-04\n",
      "For ensemble mean 6.21e-11\n",
      "Effect size: Single model d=0.224, ensemble mean d=0.404\n",
      "\n",
      "\n",
      "#### alpha_nse\n",
      "Comparison between EA-LSTM (optimized with NSE) and mHm (basin-wise calibrated)\n",
      "For single models: 1.84e-01\n",
      "For ensemble mean 7.82e-01\n",
      "Effect size: Single model d=0.049, ensemble mean d=0.047\n",
      "\n",
      "Comparison between EA-LSTM (optimized with NSE) and HBV (upper limit)\n",
      "For single models: 3.88e-02\n",
      "For ensemble mean 5.33e-01\n",
      "Effect size: Single model d=0.106, ensemble mean d=0.016\n",
      "\n",
      "\n",
      "#### beta_nse\n",
      "Comparison between EA-LSTM (optimized with NSE) and mHm (basin-wise calibrated)\n",
      "For single models: 3.24e-03\n",
      "For ensemble mean 3.24e-03\n",
      "Effect size: Single model d=0.189, ensemble mean d=0.189\n",
      "\n",
      "Comparison between EA-LSTM (optimized with NSE) and HBV (upper limit)\n",
      "For single models: 3.52e-04\n",
      "For ensemble mean 3.52e-04\n",
      "Effect size: Single model d=0.239, ensemble mean d=0.239\n",
      "\n",
      "\n",
      "#### FHV\n",
      "Comparison between EA-LSTM (optimized with NSE) and mHm (basin-wise calibrated)\n",
      "For single models: 4.16e-01\n",
      "For ensemble mean 4.81e-01\n",
      "Effect size: Single model d=0.004, ensemble mean d=0.086\n",
      "\n",
      "Comparison between EA-LSTM (optimized with NSE) and HBV (upper limit)\n",
      "For single models: 9.18e-01\n",
      "For ensemble mean 1.22e-01\n",
      "Effect size: Single model d=0.038, ensemble mean d=0.117\n",
      "\n",
      "\n",
      "#### FLV\n",
      "Comparison between EA-LSTM (optimized with NSE) and mHm (basin-wise calibrated)\n",
      "For single models: 7.31e-09\n",
      "For ensemble mean 1.10e-07\n",
      "Effect size: Single model d=0.143, ensemble mean d=0.050\n",
      "\n",
      "Comparison between EA-LSTM (optimized with NSE) and HBV (upper limit)\n",
      "For single models: 5.10e-11\n",
      "For ensemble mean 2.90e-06\n",
      "Effect size: Single model d=0.140, ensemble mean d=0.046\n",
      "\n",
      "\n",
      "#### FMS\n",
      "Comparison between EA-LSTM (optimized with NSE) and mHm (basin-wise calibrated)\n",
      "For single models: 9.25e-02\n",
      "For ensemble mean 1.02e-04\n",
      "Effect size: Single model d=0.014, ensemble mean d=0.011\n",
      "\n",
      "Comparison between EA-LSTM (optimized with NSE) and HBV (upper limit)\n",
      "For single models: 2.69e-09\n",
      "For ensemble mean 4.64e-05\n",
      "Effect size: Single model d=0.003, ensemble mean d=0.000\n"
     ]
    }
   ],
   "source": [
    "for metric in sub_metrics.keys():\n",
    "    print(f\"\\n\\n#### {metric}\")\n",
    "    print(f\"Comparison between EA-LSTM (optimized with NSE) and mHm (basin-wise calibrated)\")\n",
    "    ealstm_perf = get_mean_basin_performance(sub_metrics[metric], model=\"ealstm_NSE\")\n",
    "    _, p_val_single = wilcoxon(list(ealstm_perf.values()),\n",
    "                               list(sub_metrics[metric][\"benchmarks\"][\"mHm_basin\"].values()))\n",
    "    _, p_val_ensemble = wilcoxon(list(sub_metrics[metric][\"benchmarks\"][\"mHm_basin\"].values()), \n",
    "                                 list(sub_metrics[metric][\"ealstm_NSE\"][\"ensemble\"].values()))\n",
    "    print(f\"For single models: {p_val_single:.2e}\")\n",
    "    print(f\"For ensemble mean {p_val_ensemble:.2e}\")\n",
    "    d_single = get_cohens_d(list(ealstm_perf.values()),\n",
    "                            list(sub_metrics[metric][\"benchmarks\"][\"mHm_basin\"].values()))\n",
    "    d_ensemble = get_cohens_d(list(sub_metrics[metric][\"benchmarks\"][\"mHm_basin\"].values()), \n",
    "                              list(sub_metrics[metric][\"ealstm_NSE\"][\"ensemble\"].values()))\n",
    "    print(f\"Effect size: Single model d={d_single:.3f}, ensemble mean d={d_ensemble:.3f}\")\n",
    "\n",
    "\n",
    "    print(f\"\\nComparison between EA-LSTM (optimized with NSE) and HBV (upper limit)\")\n",
    "    _, p_val_single = wilcoxon(list(ealstm_perf.values()),\n",
    "                               list(sub_metrics[metric][\"benchmarks\"][\"HBV_ub\"].values()))\n",
    "    _, p_val_ensemble = wilcoxon(list(sub_metrics[metric][\"benchmarks\"][\"HBV_ub\"].values()), \n",
    "                                 list(sub_metrics[metric][\"ealstm_NSE\"][\"ensemble\"].values()))\n",
    "    print(f\"For single models: {p_val_single:.2e}\")\n",
    "    print(f\"For ensemble mean {p_val_ensemble:.2e}\")\n",
    "    d_single = get_cohens_d(list(ealstm_perf.values()),\n",
    "                            list(sub_metrics[metric][\"benchmarks\"][\"HBV_ub\"].values()))\n",
    "    d_ensemble = get_cohens_d(list(sub_metrics[metric][\"benchmarks\"][\"HBV_ub\"].values()), \n",
    "                              list(sub_metrics[metric][\"ealstm_NSE\"][\"ensemble\"].values()))\n",
    "    print(f\"Effect size: Single model d={d_single:.3f}, ensemble mean d={d_ensemble:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
